45771
Description
Mind Map by Keith Walker, updated more than 1 year ago
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Created by Keith Walker
over 11 years ago
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Multiple Linear Regression
- Y = a + b₁x₁ + b₂x₂.....bkxk
- xᵢ is the ᵢth explanatory
variable (i= 1,2,3...k)
- Y is mean,
expected or
predicted value of y
which corresponds
to a group of values
for x
- Partial Regression
Coefficients where b₁
represents the amount Y
equals when b₁ is
increased by 1 unit and all
other coefficients remain
the same.
- xᵢ is the ᵢth explanatory
variable (i= 1,2,3...k)
- Analysis
- Goodness of fit. Adjusted R². Lo = poor
- ANOVA ie H₀
b₁,b₂.....bᵢ =0
Significant result, at
least one x is linear
related to Y
- t-test of each coefficient. Same as for
Linear Regression for each explanatory
variable. b₁/SE(b₁)
- Goodness of fit. Adjusted R². Lo = poor
- When we are
interested in the effect
of several explanatory
variables on a
dependant variable.
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